We implement our model based on the PyTorch [1]} library and all the experiments are conducted on a single NVIDIA Tesla V100 GPU. We adopt SeqNet [2]} as our baseline network. During training, the batch size is set to 5 and each image is resized to 900\(\times \) 1500 on CUHK-SYSU [3]} and PRW [4]}. We follow [3]} to set the circular queue size of OIM to 5,000/500/2,000. The only data augmentation used here is random horizontal flip. We follow our baseline model [2]} in the settings of some hyper-parameters: Our model is optimized by Stochastic Gradient Descent (SGD) for 18 epochs on CUHK-SYSU, PRW, and MovieNet-CS with the initial learning rate of 0.003 which is warmed up during the first epoch and decreased by 10 at the 16th epoch. The momentum and weight decay of SGD are set to 0.9 and 5\(\times 10^{-4}\)  individually. At test time, the thresholds of NMS are set to 0.4 and 0.5 w.r.t. the first and second heads, respectively. The parameters of the CBGM module used in SeqNet also follow the same settings as in [2]}, i.e., \( k1/k2 = 10/3 \)  and \( k1/k2 = 30/4 \)  on CUHK-SYSU and PRW, respectively.
